Advanced SearchSearch Tips
Moving Shadow Detection using Deep Learning and Markov Random Field
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Moving Shadow Detection using Deep Learning and Markov Random Field
Lee, Jong Taek; Kang, Hyunwoo; Lim, Kil-Taek;
  PDF(new window)
We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.
Moving Shadow Detection;Convolutional Neural Network;Markov Random Field;Surveillance System;Object Detection;
 Cited by
CNN 기반의 와일드 환경에 강인한 고속 얼굴 검출 방법,송주남;김형일;노용만;

한국멀티미디어학회논문지, 2016. vol.19. 8, pp.1310-1319 crossref(new window)
Fast and Robust Face Detection based on CNN in Wild Environment, Journal of Korea Multimedia Society, 2016, 19, 8, 1310  crossref(new windwow)
Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel, “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Computation, Vol. 1, No. 4, pp. 541-551, 1989. crossref(new window)

M.S. Ryoo, “How the Computer Vision Researchers survive in an era of Deep Learning,” Journal of the Institute of Electronics and Information Engineers, Vol. 42, No. 5, pp. 29-33, 2015.

A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Proceedings of Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.

A. Prati, I. Mikic, M.M. Trivedi, and R. Cucchiara, “Detecting Moving Shadows:Algorithms and Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 7, pp. 918-923, 2003. crossref(new window)

S. Yeon and J. Kim, “Robust Illumination Change Detection Using Image Intensity and Texture,” Journal of Korea Multimedia Society, Vol. 16, No. 2, pp. 169-179, 2013 crossref(new window)

J.T. Lee, K.T. Lim, and Y. Chung, "Moving Shadow Detection from Background Image and Deep Learning," Proceedings of PSIVT 2015 Workshop on Video Surveillance, 2015.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, pp. 2274-2282, 2012. crossref(new window)

M.V. den Bergh, X. Boix, G. Roig, B. de Capitani, and L.V. Gool, "SEEDS: Superpixels Extracted via Energy-Driven Sampling," Proceeding of European Conference on Computer Vision, pp. 13-26, 2012.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, et al., "Caffe: Convolutional Architecture for Fast Feature Embedding," Proceeding of the ACM International Conference on Multimedia, pp. 675-678, 2014.

R. Qin, S. Liao, Z. Lei, and S.Z. Li, "Moving Cast Shadow Removal based on Local Descriptors," Proceeding of 20th International Conference on Pattern Recognition, pp. 1377-1380, 2010.

N. Martel-Brisson and A. Zaccarin, "Kernelbased Learning of Cast Shadows from a Physical Model of Light Sources and Surfaces for Low-level Segmentation," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.

CAVIAR: Context Aware Vision using Imagebased Active Recognition, (2005) http://homepages. (accessed Oct. 1, 2015).

J. Dai and D. Han, “Region-based Moving Shadow Detection using Affinity Propagation,” International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 8, No. 3, pp. 65-74, 2015. crossref(new window)